Tower Top Daily Average
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.5 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.1.0 v forcats 0.5.1
## Warning: package 'readr' was built under R version 4.1.2
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
top <- read.csv("Tower Top RH.csv")
# top <- top[,-1]
# top <- separate(top, Time, into = c('hours','minutes'))
# top$hours <- as.numeric(top$hours)
# top <- na.omit(top)
# top$hours <- as.numeric(top$hours)
# top$minutes <- as.numeric(top$minutes)
#
# for (i in 1:nrow(top)) {
# if (top$minutes[i] == 30| top$minutes[i] == 45) {
# top$hours[i] <- top$hours[i] + 0.5
# }
# }
#
# top <- top %>% mutate(RH = case_when(top$Logger == 66 ~ (0.7668 * top$RH) + 26.667, top$Logger == 586 ~ (0.8424 * top$RH) + 6.4224, top$Logger == 5306 ~ (1.198 * top$RH) - 18.808, top$Logger == 5305 ~ (0.8395 * top$RH) + 17.9339))
#
top_sum <- top %>% group_by(hours) %>% summarize(Humidity = mean(RH, na.rm=T))
top$Location <- "Top"
top_sum$Location <- "Top"
date_range <- seq(as.Date('2018-01-01'), as.Date('2022-12-31'), by = 1)
missingdates <- date_range[!date_range %in% top$dates]
ggplot(top_sum, aes(hours, Humidity)) + geom_point(aes(x = hours, y = Humidity)) + geom_line() + geom_errorbar(alpha = 0.3, aes(ymin = Humidity - (sd(Humidity, na.rm = T) / sqrt(nrow(top_sum))), ymax = Humidity +(sd(Humidity, na.rm = T) / sqrt(nrow(top_sum))), width = 0.2))
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 row(s) containing missing values (geom_path).
# missingdates <- data.frame(missingdates)
# missingdates[,c(2:7)] <- NA
# colnames(missingdates) <- colnames(top)
# missingdates$Date <- as.Date(missingdates$Date)
# top$Date <- as.Date(top$Date)
#
# top <- top %>% full_join(missingdates,by="Date")
#
# top <- top[,c(1:7)]
#
# colnames(top) <- colnames(missingdates)
#
# top %>% arrange(Date)
# top$RS <- "Inside RS"
# top$orientation <- "NA"
#
# write.csv(top, "Tower Top RH.csv")
Tower Middle Daily Average
library(tidyverse)
middle <- read.csv('Tower Middle RH.csv')
# middle <- middle[,-c(1,2)]
#
# middle <- separate(middle, Time, into = c('hours','minutes'))
# middle$hours <- as.numeric(middle$hours)
# middle$Temp <- ((middle$Temp - 32)*5)/9
# middle <- na.omit(middle)
# middle$hours <- as.numeric(middle$hours)
# middle$minutes <- as.numeric(middle$minutes)
#
# for (i in 1:nrow(middle)) {
# if (middle$minutes[i] == 30 | middle$minutes[i] == 45) {
# middle$hours[i] <- middle$hours[i] + 0.5
# }
# }
#
# middle$RH <- (middle$RH * 1.000177) + 0.003496
date_range <- seq(as.Date('2018-01-01'), as.Date('2022-12-31'), by = 1)
missingdates <- date_range[!date_range %in% middle$dates]
middle_sum <- middle %>% group_by(hours) %>% summarize(Humidity = mean(RH, na.rm=T))
middle$Location <- "Middle"
middle_sum$Location <- "Middle"
ggplot(middle_sum, aes(hours, Humidity)) + geom_point(aes(x = hours, y = Humidity)) + geom_line()+ geom_errorbar(alpha = 0.3, aes(ymin = Humidity - (sd(Humidity, na.rm = T) / nrow(middle_sum)), ymax = Humidity + (sd(Humidity, na.rm = T) / nrow(middle_sum)) , width = 0.2)) + labs(title = "Tower Middle Relative Humidity")
# missingdates <- data.frame(missingdates)
# missingdates[,c(2:8)] <- NA
# colnames(missingdates) <- colnames(middle)
# missingdates$Date <- as.Date(missingdates$Date)
# middle$Date <- as.Date(middle$Date)
#
# middle <- middle %>% full_join(missingdates,by="Date")
#
# middle <- middle[,c(1:8)]
#
# colnames(middle) <- colnames(missingdates)
#
# middle %>% arrange(Date)
#
# # 20180724-20180923 varied
# middle$RS <- "Inside RS"
# library(tidyverse)
# middle <- middle %>%
# rename(dates = Date)
# middle$RS[middle$dates <= "2018-09-23"] <- "varied"
# middle$Orientation <- "NA"
#
# write.csv(middle, "Tower Middle RH.csv")
Tower Base Daily Average
base <- read.csv('Tower Base RH.csv')
# base <- base[,-c(1,2)]
# base$Logger <- '4377'
#
# base <- separate(base, Time, into = c('hours','minutes'))
# base$hours <- as.numeric(base$hours)
# base$Temp <- ((base$Temp - 32)*5)/9
# base$hours <- as.numeric(base$hours)
# base$minutes <- as.numeric(base$minutes)
#
# for (i in 1:nrow(base)) {
# if (base$minutes[i] == 30 | base$minutes[i] == 45) {
# base$hours[i] <- base$hours[i] + 0.5
# }
# }
#
# base$RH <- (base$RH * 0.9473) + 5.4224
date_range <- seq(as.Date('2018-01-01'), as.Date('2022-12-31'), by = 1)
missingdates <- date_range[!date_range %in% base$Date]
base_sum <- base %>% group_by(hours) %>% summarise(Humidity = mean(RH, na.rm = TRUE))
base$Location <- "Base"
base_sum$Location <- "Base"
ggplot(base_sum, aes(hours, Humidity)) + geom_point(aes(x = hours, y = Humidity)) + geom_line() + geom_errorbar(alpha = 0.3, aes(ymin = Humidity - (sd(Humidity, na.rm = T) / sqrt(nrow(base_sum))), ymax = Humidity +(sd(Humidity, na.rm = T) / sqrt(nrow(base_sum))), width = 0.2)) + labs(title = "Tower Base Relative Humidity")
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 row(s) containing missing values (geom_path).
# missingdates <- data.frame(missingdates)
# missingdates[,c(2:7)] <- NA
# colnames(missingdates) <- colnames(base)
# missingdates$Date <- as.Date(missingdates$Date)
# base$Date <- as.Date(base$Date)
#
# base <- base %>% full_join(missingdates,by="Date")
#
# base <- base[,c(1:7)]
#
# colnames(base) <- colnames(missingdates)
#
# base %>% arrange(Date)
#
# base$RS <- "Inside RS"
# base$Orientation <- "NA"
#
# write.csv(base, "Tower Base RH.csv")
MP150 Plot Daily Average
MP150 <- read.csv("MP150 RH.csv")
# MP150 <- MP150[,-c(1,2)]
# MP150 <- separate(MP150, Date...Time, into = c("dates", "Time"), sep = " ")
# MP150 <- separate(MP150, Time, into = c('hours','minutes'))
# MP150$dates <- as.Date(MP150$dates, "%m/%d/%Y")
# MP150$hours <- as.numeric(MP150$hours)
# MP150 <- na.omit(MP150)
#
#
# for (i in 1:nrow(MP150)) {
# if (MP150$minutes[i] == 30 | MP150$minutes[i] > 30) {
# MP150$hours[i] <- MP150$hours[i] + 0.5
# }
# }
#
# MP150 <- MP150 %>% mutate(Humidity = case_when(MP150$Logger == 5354 ~ (0.9764 * MP150$Humidity) + 0.2417, MP150$Logger == 7432 ~ (0.898 * MP150$Humidity) + 10.075))
MP150_sum <- MP150 %>% group_by(hours) %>% summarize(Humidity = mean(Humidity))
MP150$Location <- "MP150"
MP150_sum$Location <- "MP150"
ggplot(MP150_sum, aes(hours, Humidity)) + geom_point(aes(x = hours, y = Humidity)) + geom_line() + geom_errorbar(alpha = 0.3, aes(ymin = Humidity - (sd(Humidity, na.rm = T) / sqrt(nrow(MP150_sum))), ymax = Humidity +(sd(Humidity, na.rm = T) / sqrt(nrow(MP150_sum))), width = 0.2)) + labs(title = "MP150 Relative Humidity")
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 row(s) containing missing values (geom_path).
# date_range <- seq(as.Date('2018-01-01'), as.Date('2022-12-31'), by = 1)
# missingdates <- date_range[!date_range %in% MP150$dates]
# missingdates <- data.frame(missingdates)
# missingdates[,c(2:7)] <- NA
# colnames(missingdates) <- colnames(MP150)
# missingdates$dates <- as.Date(missingdates$dates)
# MP150$dates <- as.Date(MP150$dates)
#
# MP150 <- MP150 %>% full_join(missingdates,by="dates")
#
# MP150 <- MP150[,c(1:7)]
#
# colnames(MP150) <- colnames(missingdates)
#
# MP150 %>% arrange(dates)
#
# # 20180726-20180923 varied
# MP150$RS <- "Inside RS"
# MP150$RS[MP150$dates <= "2018-09-23"] <- "varied"
# MP150$Orientation <- "NA"
# falldates <- seq(as.Date('2018-12-08'), as.Date('2019-01-07'), by = 1)
# falldates <- data.frame(falldates)
# falldates[,c(2:9)] <- NA
# MP150<- MP150[-c(1)]
# colnames(falldates) <- colnames(MP150)
# falldates$dates <- as.Date(falldates$dates)
# MP150$dates <- as.Date(MP150$dates)
# MP150[MP150$dates >= "2018-12-08" & MP150$dates <= "2019-01-07", c(2:5)] <- NA
# MP150 <- MP150[,c(1:9)]
# colnames(MP150) <- colnames(falldates)
# write.csv(MP150, "MP150 RH.csv")
Lago475 Plot Daily Average
library(tidyverse)
Lago475 <- read.csv("Lago 475 RH.csv")
# Lago475 <- Lago475[,-c(1,2)]
# Lago475 <- separate(Lago475, Date...Time, into = c("dates", "Time"), sep = " ")
# Lago475$dates <- as.Date(Lago475$dates, "%m/%d/%Y")
# Lago475 <- separate(Lago475, Time, into = c('hours','minutes'))
# Lago475$hours <- as.numeric(Lago475$hours)
#
# for (i in 1:nrow(Lago475)) {
# if (Lago475$minutes[i] == 30 | Lago475$minutes[i] == 45) {
# Lago475$hours[i] <- Lago475$hours[i] + 0.5
# }
# }
#
# Lago475 <- Lago475 %>% mutate(Humidity = case_when(Lago475$Logger == 0066 ~ (1.297 * Lago475$Humidity) - 34.067, Lago475$Logger == 5353 ~ (1 * Lago475$Humidity)))
Lago475_sum <- Lago475 %>% group_by(hours) %>% summarise(Humidity = mean(Humidity, na.rm = T))
Lago475$Location <- "Lago 475"
Lago475_sum$Location <- "Lago 475"
ggplot(Lago475_sum, aes(hours, Humidity))+ geom_point(aes(x = hours, y = Humidity)) + geom_line() + geom_errorbar(alpha = 0.3, aes(ymin = Humidity - (sd(Humidity, na.rm = T) / sqrt(nrow(Lago475_sum))), ymax = Humidity +(sd(Humidity, na.rm = T) / sqrt(nrow(Lago475_sum))), width = 0.2)) + labs(title = "Lago Relative Humidity")
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 row(s) containing missing values (geom_path).
# date_range <- seq(as.Date('2018-01-01'), as.Date('2022-12-31'), by = 1)
# missingdates <- date_range[!date_range %in% Lago475$dates]
# missingdates <- data.frame(missingdates)
# missingdates[,c(2:7)] <- NA
# colnames(missingdates) <- colnames(Lago475)
# missingdates$dates <- as.Date(missingdates$dates)
# Lago475$dates <- as.Date(Lago475$dates)
#
# Lago475 <- Lago475 %>% full_join(missingdates,by="dates")
#
# Lago475 <- Lago475[,c(1:7)]
#
# colnames(Lago475) <- colnames(missingdates)
#
# Lago475 %>% arrange(dates)
#
# Lago475$RS <- "Inside RS"
# Lago475$Orientation <- "NA"
#
# write.csv(Lago475, "Lago 475 RH.csv")
Parahuaco Plot Daily Average
Parahuaco <- read.csv("Parahuaco RH.csv")
# Parahuaco$Logger <- "6889"
# Parahuaco <- Parahuaco[,-c(1,2)]
# Parahuaco <- separate(Parahuaco, Date...Time, into = c("dates", "Time"), sep = " ")
# Parahuaco$dates <- as.Date(Parahuaco$dates, "%m/%d/%Y")
# Parahuaco <- separate(Parahuaco, Time, into = c('hours','minutes'))
# Parahuaco$hours <- as.numeric(Parahuaco$hours)
#
# for (i in 1:nrow(Parahuaco)) {
# if (Parahuaco$minutes[i] == 30| Parahuaco$minutes[i] == 45) {
# Parahuaco$hours[i] <- Parahuaco$hours[i] + 0.5
# }
# }
#
# Parahuaco <- Parahuaco %>% mutate(Humidity = case_when(Parahuaco$Logger == 6889 ~ (0.993 * Parahuaco$Humidity) -1.752))
Parahuaco_sum <- Parahuaco %>% group_by(hours) %>% summarise(Humidity = mean(Humidity, na.rm = T))
Parahuaco$Location <- "Parahuaco"
Parahuaco_sum$Location <- "Parahuaco"
ggplot(Parahuaco_sum, aes(hours, Humidity))+ geom_point(aes(x = hours, y = Humidity)) + geom_line() + geom_errorbar(alpha = 0.3, aes(ymin = Humidity - (sd(Humidity, na.rm = T) / sqrt(nrow(Parahuaco_sum))), ymax = Humidity +(sd(Humidity, na.rm = T) / sqrt(nrow(Parahuaco_sum))), width = 0.2)) + labs(title = "Parahuaco Relative Humidity")
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 row(s) containing missing values (geom_path).
# date_range <- seq(as.Date('2018-01-01'), as.Date('2022-12-31'), by = 1)
# missingdates <- date_range[!date_range %in% Parahuaco$dates]
# missingdates <- data.frame(missingdates)
# missingdates[,c(2:7)] <- NA
# colnames(missingdates) <- colnames(Parahuaco)
# missingdates$dates <- as.Date(missingdates$dates)
# Parahuaco$dates <- as.Date(Parahuaco$dates)
#
# Parahuaco <- Parahuaco %>% full_join(missingdates,by="dates")
#
# Parahuaco <- Parahuaco[,c(1:7)]
#
# colnames(Parahuaco) <- colnames(missingdates)
#
# Parahuaco %>% arrange(dates)
#
# Parahuaco$RS <- "Inside RS"
# Parahuaco$Orientation <- "NA"
# Parahuaco[Parahuaco$dates > "2019-08-18", c(3:6)] <- NA
# Parahuaco<- Parahuaco[-c(1)]
# write.csv(Parahuaco, "Parahuaco RH.csv")
GP Plot Daily Average
GP <- read.csv("GP RH.csv")
# GP$Logger <- "5355"
# GP <- GP[,-c(1,2)]
# GP <- separate(GP, Date...Time, into = c("dates", "Time"), sep = " ")
# GP$dates <- as.Date(GP$dates, "%m/%d/%Y")
# GP <- separate(GP, Time, into = c('hours','minutes'))
# GP$hours <- as.numeric(GP$hours)
#
# for (i in 1:nrow(GP)) {
# if (GP$minutes[i] == 30| GP$minutes[i] == 45) {
# GP$hours[i] <- GP$hours[i] + 0.5
# }
# }
#
# GP <- GP %>% mutate(Humidity = case_when(GP$Logger == 5355 ~ (0.9187 * GP$Humidity) + 11.8054))
GP_sum <- GP %>% group_by(hours) %>% summarise(Humidity = mean(Humidity, na.rm = T))
GP$Location <- "GP"
GP_sum$Location <- "GP"
ggplot(GP_sum, aes(hours, Humidity)) + geom_point(aes(x = hours, y = Humidity)) + geom_line() + geom_errorbar(alpha = 0.3, aes(ymin = Humidity - (sd(Humidity, na.rm = T) / sqrt(nrow(GP_sum))), ymax = Humidity +(sd(Humidity, na.rm = T) / sqrt(nrow(GP_sum))), width = 0.2)) + labs(title = "GP Relative Humidity")
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 row(s) containing missing values (geom_path).
#
# date_range <- seq(as.Date('2018-01-01'), as.Date('2022-12-31'), by = 1)
# missingdates <- date_range[!date_range %in% GP$dates]
# missingdates <- data.frame(missingdates)
# missingdates[,c(2:7)] <- NA
# colnames(missingdates) <- colnames(GP)
# missingdates$dates <- as.Date(missingdates$dates)
# GP$dates <- as.Date(GP$dates)
#
# GP <- GP %>% full_join(missingdates,by="dates")
#
# GP <- GP[,c(1:7)]
#
# colnames(GP) <- colnames(missingdates)
#
# GP %>% arrange(dates)
#
# GP$RS <- "Inside RS"
# GP$Orientation <- "NA"
#
# write.csv(GP, "GP RH.csv")
Murcielago (Rest) Daily Average
library(dplyr)
library(tidyverse)
Murcielago <- read.csv("Murcielago RH.csv")
# Murcielago$Logger <- "5306"
# Murcielago <- Murcielago[,-1]
# Murcielago <- separate(Murcielago, Date...Time, into = c("dates", "Time"), sep = " ")
# Murcielago$dates <- as.Date(Murcielago$dates, "%m/%d/%Y")
# Murcielago <- separate(Murcielago, Time, into = c('hours','minutes'))
# Murcielago$hours <- as.numeric(Murcielago$hours)
# Murcielago <- na.omit(Murcielago)
#
# for (i in 1:nrow(Murcielago)) {
# if (Murcielago$minutes[i] == 30| Murcielago$minutes[i] == 45) {
# Murcielago$hours[i] <- Murcielago$hours[i] + 0.5
# }
# }
#
# Murcielago <- Murcielago %>% mutate(Humidity = case_when(Murcielago$Logger == 5306 ~ (1.198 * Murcielago$Humidity) - 20.808))
Murcielago_sum <- Murcielago %>% group_by(hours) %>% summarise(Humidity = mean(Humidity, na.rm = T))
Murcielago$Location <- "Murcielago"
Murcielago_sum$Location <- "Murcielago"
ggplot(Murcielago_sum, aes(hours, Humidity)) + geom_point(aes(x = hours, y = Humidity)) + geom_line() + geom_errorbar(alpha = 0.3, aes(ymin = Humidity - (sd(Humidity, na.rm = T) / sqrt(nrow(Murcielago_sum))), ymax = Humidity +(sd(Humidity, na.rm = T) / sqrt(nrow(Murcielago_sum))), width = 0.2)) + labs(title = "Murcielago Relative Humidity")
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 row(s) containing missing values (geom_path).
# date_range <- seq(as.Date('2018-01-01'), as.Date('2022-12-31'), by = 1)
# missingdates <- date_range[!date_range %in% Murcielago$dates]
# missingdates <- data.frame(missingdates)
# missingdates[,c(2:8)] <- NA
# colnames(missingdates) <- colnames(Murcielago)
# missingdates$dates <- as.Date(missingdates$dates)
# Murcielago$dates <- as.Date(Murcielago$dates)
#
# Murcielago <- Murcielago %>% full_join(missingdates, by="dates")
#
# Murcielago <- Murcielago[,c(1:8)]
#
# colnames(Murcielago) <- colnames(missingdates)
#
# Murcielago %>% arrange(dates)
# #
# Murcielago$RS <- "Inside RS"
# Murcielago$RS[Murcielago$dates <= "2018-09-23"] <- "RS open"
# Murcielago$Orientation <- "NA"
#
# Murcielago <- Murcielago %>%
# filter(dates <= "2018-10-19")
#
#
# write.csv(Murcielago, "Murcielago RH.csv")
Danta (Rest) Daily Average
Danta <- read.csv("Danta Region RH.csv")
# Danta <- Danta[,-c(1,2)]
# Danta <- separate(Danta, Date.Time, into = c("dates", "Time"), sep = " ")
# Danta$dates <- as.Date(Danta$dates, "%m/%d/%Y")
# Danta <- separate(Danta, Time, into = c('hours','minutes'))
# Danta$hours <- as.numeric(Danta$hours)
#
# for (i in 1:nrow(Danta)) {
# if (Danta$minutes[i] == 30| Danta$minutes[i] == 45) {
# Danta$hours[i] <- Danta$hours[i] + 0.5
# }
# }
#
# Danta <- Danta %>% mutate(RH = case_when(Danta$Logger == 7432 ~ (0.898 * Danta$RH) + 10.075, Danta$Logger == 5361 ~ (0.8395 * Danta$RH) + 17.9339))
Danta_sum <- Danta %>% group_by(hours) %>% summarise(Humidity = mean(RH, na.rm = T))
Danta$Location <- "Danta"
Danta_sum$Location <- "Danta"
ggplot(Danta_sum, aes(hours, Humidity)) + geom_point(aes(x = hours, y = Humidity)) + geom_line() + geom_errorbar(alpha = 0.3, aes(ymin = Humidity - (sd(Humidity, na.rm = T) / sqrt(nrow(Danta_sum))), ymax = Humidity +(sd(Humidity, na.rm = T) / sqrt(nrow(Danta_sum))), width = 0.2)) + labs(title = "Danta Relative Humidity")
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 row(s) containing missing values (geom_path).
# date_range <- seq(as.Date('2018-01-01'), as.Date('2022-12-31'), by = 1)
# missingdates <- date_range[!date_range %in% Danta$dates]
# missingdates <- data.frame(missingdates)
# missingdates[,c(2:7)] <- NA
# colnames(missingdates) <- colnames(Danta)
# missingdates$dates <- as.Date(missingdates$dates)
# Danta$dates <- as.Date(Danta$dates)
#
# Danta <- Danta %>% full_join(missingdates, by="dates")
#
# Danta <- Danta[,c(1:7)]
#
# colnames(Danta) <- colnames(missingdates)
#
# Danta %>% arrange(dates)
#
# Danta$RS <- "Inside RS"
# Danta$Orientation <- "NA"
#
# write.csv(Danta, "Danta Region RH.csv")
Tower middle daily average temperature and humidity
library(reshape2)
##
## Attaching package: 'reshape2'
## The following object is masked from 'package:tidyr':
##
## smiths
library(ggplot2)
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
middle3 <- middle %>% group_by(hours) %>% summarize(Average_humidity = mean(RH, na.rm=T), Average_temp = mean(Temp, na.rm=T))
middle3$Average_temp <- middle3$Average_temp + 60
df <- melt(middle3 , id.vars = 'hours', variable.name = 'series')
ggplot(df, aes(hours, value)) +
geom_point(aes(color = series)) + geom_line(aes(color = series)) + labs(title = "Tower Middle Humidity vs Temperature") + scale_y_continuous("Humidity", sec.axis = sec_axis(~ . - 60, name = "Temperature"))
Comparison of Tower middle and Danta rest relative humidity
library(reshape2)
# Danta <- separate(dataset, Date...Time, into = c("dates", "Time"), sep = " ")
# Danta$dates <- as.Date(Danta$dates, "%m/%d/%Y")
# Danta <- separate(Danta, Time, into = c('hours','minutes'))
# Danta$hours <- as.numeric(Danta$hours)
#
# for (i in 1:nrow(Danta)) {
# if (Danta$minutes[i] == 30 | Danta$minutes[i]==45) {
# Danta$hours[i] <- Danta$hours[i] + 0.5
# }
# }
#
# Danta <- Danta %>% mutate(RH2 = case_when(Danta$Logger == 7432 ~ (0.898 * Danta$Humidity) + 10.075))
TM_D <- middle_sum %>% left_join(Danta_sum, by = "hours")
df <- TM_D %>%
select(c(hours, Humidity.x, Humidity.y)) %>%
rename(RH_middle = Humidity.x, RH_danta = Humidity.y)
df <- melt(df , id.vars = 'hours', variable.name = 'series')
ggplot(df, aes(hours, value)) +
geom_point(aes(color = series)) + geom_line(aes(color = series)) + labs(title = "Calibrated Tower Middle vs Danta Humidity")
library(plotly)
AllSites <- rbind(top_sum, middle_sum, base_sum, Danta_sum, Parahuaco_sum, Murcielago_sum, GP_sum)
allplots <- ggplot(AllSites, aes(hours, Humidity, color = Location)) + geom_smooth(se = F, size = 0.35)
ggplotly(allplots)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 6 rows containing non-finite values (stat_smooth).
Comparison of Tower middle and Foraging relative humidity
library(reshape2)
library(tidyverse)
foraging <- rbind(Parahuaco, Lago475, MP150, GP)
#foraging <- separate(foraging, Date...Time, into = c("dates", "Time"), sep = " ")
#foraging$dates <- as.Date(foraging$dates, "%m/%d/%Y")
#foraging <- separate(foraging, Time, into = c('hours','minutes'))
# foraging$hours <- as.numeric(foraging$hours)
#
# for (i in 1:nrow(foraging)) {
# if (foraging$minutes[i] == 30 | foraging$minutes[i] == 45) {
# foraging$hours[i] <- foraging$hours[i] + 0.5
# }
# }
foraging <- foraging %>% group_by(hours) %>% summarize(Humidity = mean(Humidity, na.rm = T))
foraging$Place <- "foraging"
TM_F <- middle_sum %>%
left_join(foraging, by = "hours")
df <- TM_F %>%
select(c(hours, Humidity.x, Humidity.y)) %>%
rename(RH_middle = Humidity.x, RH_foraging = Humidity.y)
df <- melt(df, id.vars = 'hours', variable.name = 'series')
ggplot(df, aes(hours, value)) +
geom_point(aes(color = series)) + geom_line(aes(color = series)) + labs(title = "Uncalibrated Tower Middle vs Foraging Humidity")
